Abstract

Most neural networks proposed for pattern recognition sample the incoming image at one instant and then analyse it. This means that the data to be analysed is limited to that containing the noise present at one instant. Time independent noise is therefore, captured but only one sample of time dependent noise is included in the analysis. If however, the incoming image is sampled at several instants, or continuously, then in the subsequent analysis the time dependent noise can be averaged out. This, of course, assumes that sufficient samples can be taken before the object being imaged, has moved an appreciable distance in the field of view. High speed sampling requires parallel image input and is most conveniently carried out by optoelectronic neural network image analysis systems. Optical technology is particularly good at performing certain operations, such as Fourier Transforms, correlations and convolutions while others such as subtraction are difficult. So for an optical net it is best to choose an architecture based on convenient operations such as the high order neural networks.